We present a novel weighted approach for shrinkage functions learning in image denoising. The proposed approach optimizes the shape of the shrinkage functions and maximizes denois...
This paper suggests a discriminative approach for wavelet denoising
where a set of mapping functions (MF) are applied to the transform
coefficients in an attempt to produce a noi...
Overcomplete representations are attracting interest in image processing theory, particularly due to their potential to generate sparse representations of data based on their morp...
Sparse Orthonormal Transforms (SOT) has recently been proposed as a data compression method that can achieve sparser representations in transform domain. Given initial conditions,...
An image representation framework based on structured sparse model selection is introduced in this work. The corresponding modeling dictionary is comprised of a family of learned ...